1. Field of the Invention
The present invention relates to a control and a method for determining system control parameters of an effect to be controlled in industrial processes, plants and mechanical systems. Particularly, the present invention relates to a control system rising desired algorithms for determining the control parameters of the object easily, quickly and reliably.
2. Description of the Prior Art
For control systems employed in various industrial plants and mechanical systems such as manipulators, it is essential to properly determine their control parameters such as PID controller's constants so that objects to be controlled may provide desired responses.
To determine the control parameters of the plants and the like computer aided control systems have been developed. The computer aided control systems are designed to readily handle various kinds of algorithms.
In the computer aided control system, a mathematical model, i.e., a transfer function is needed to express the dynamic characteristics of the to be controlled object. To obtain the transfer function, several algorithms are known.
One of these algorithms expresses the dynamic characteristics of the object with differential equations according to the physical structure of the object and obtains the transfer function by linear approximation.
Another one inputs a test signal to the object, to obtain a response signal. Based on the input and response signal, a time series model, i.e., a pulse transfer function is obtained, according to the least square method.
A further one inputs a sinusoid wave signal to the object. Based on the amplitude ratio and phase difference of the sinusoid wave signal and the basic wave component of an output signal, a frequency response curve of the object is obtained. (For example, a scientific journal, "System Identification" by Akizuki, Katayama, Sagara and Nakamizo, published by the Society of Instrument and Control Engineers (SICE), in Japan, in 1981.
To determine the control parameters of the plants and the like, the following algorithms are well known:
(1) Ziegler-Nichols method by J. G. Ziegler and N. B. Nichols, disclosed in "Optimum Settings for Automatic Controllers," Trans., ASME, Vol. 64 (1942), pp. 759 to 768;
(2) CHR method by Kun Li Chien, J. A. Hrones and J. B. Reswick, disclosed in "On the Automatic Control of Generalized Passive Systems," Trans., ASME, Vol. 74 (1952), pp. 175 to 185; and
(3) Partial model matching method by Kitamori, disclosed in "A method of control system design based upon partial knowledge about controlled processes" Trans. Society of Instrument and Control Engineers, theses Vol. 15 (1979), No. 4, pp. 549 to 555 in Japanese.
To determine the control parameters according to conventional PID (Proportional Integral Derivative) control that is widely employed in an industrial field, the partial model matching method is also effective as the Ziegler-Nichols and CHR methods. In addition, the partial model matching method is advantageous because it is easily adaptable to a decoupling PID control for a multiple-input-output process (Kitamori: "A Design Method for I-PD Type Decoupled Control Systems Based upon Partial Knowledge about Controlled Processes" Trans. Society of Instruments and Control Engineers) and a sampled value control (Kitamori: "A Design Method for Sampled Data Control Systems Based upon Partial Knowledge about Controlled Process" Trans. Society of Instruments and Control Engineers, Vol 15, No. 5 pp. 695-700 (1979).
However, in the actual control system, a continuous-time dynamic characteristic model, i.e., a transfer function of the controlled object is needed. In addition, the type of the transfer function is limited by the algorithms to be selected. For example, for the Ziegler-Nichols method, the transfer function has the following form: EQU G(S)=(K/S)e.sup.-LS ( 1)
where S is the Laplace Operator.
For the CHR method, the transfer function has the following form: EQU G(S)=(K/(1+TS))e.sup.-LS ( 2)
The partial model matching method is applicable to the following types of transfer functions: EQU G(S)=B(S)/A(S) EQU A(S)=a.sub.0 +a.sub.1 S+a.sub.2 S.sup.2 + . . . +a.sub.n S.sup.n EQU B(S)=b.sub.0 +b.sub.1 S+ . . . +b.sub.m S.sup.m ( 3)
However, if a transfer function has a zero point, such as in an overshoot system, or if the transfer function has complex poles, namely, oscillatory direct response application of the partial model matching method may result in providing control parameters which makes the control system unstable in operation.
In obtaining a transfer function according to the algorithms previously explained, it is not certain whether or not the obtained transfer function has a form suitable for the control system. For example, a transfer function obtainable from the physical structure of the object to be controlled is generally of high degree. A time series model estimated by the least square method, i.e., a pulse transfer function, is not directly applicable to the control systems. A frequency response curve measured as to the object is not directly applicable to the control system.
Therefore, to apply the transfer functions to the control system, each of the transfer functions shall be converted into a continuous-time transfer function and then converted into a simple form, such as the form of equation (1) or (2). Namely, there is a need to provide means to reduce the degree of the transfer function.
To reduce the degree of the transfer function, various algorithms have been proposed. For example, "Automatic Control Handbook (Basics)" edited by Society of Instrument and Control Engineers in 1983, pp. 85 to 89 may be referred to. However, it is not clear which of the algorithms is most effective in the control system.